Take best practices for a large campaign and figure how to scale them for a small campaign.

Use long tail targeting to generate cost-effective leads. If you’re a plumber instead of competing against an expensive high volume term like “plumber” target numerous long-tail terms like “broken drain pipe”, “sink installation”, etc. Use the same technique to target geography. Instead of Chicago target a neighborhood like Rogers Park or a zipcode like 60201 (see my post on seo for neighborhood names and state abbreviations).

Yahoo has great interface for figuring out geotargeting at a regional level.

Google allows you to target a radius around a fixed point. This doesn’t work well for businesses with small service areas (e.g. under 2 mi radius) so use “DIY” targeting where you can draw an area to target on a map.

Use Keyword Expansion to Geotarget Keywords. For example target “best restaurants in Pleasanton” and “dining in Pleasanton”, etc.

Don’t forget demographics. Different areas have different ways of searching.

Jon Kelly, SureHits
Specializes in financial services search advertising. They need to do geotargeting because of different state by state regulations.
How to do search engine advertising:

1. Calculate Click Value: Conversion x Conversion Value = Click Value

2. Reward The User’s Choices: Make sure the message of the landing page matches the search query/ad copy

3. Watch Your Data: Homonyms can kill you. He did a campaign targeting “mobile home loans” and showed up against search querys for “Mobile, Alabama” which killed conversions.

- cost goes up but at some point it decreases (e.g. a very long tail keyword is very relevant but likely low cost)

- volume increases but then decreases

You need balance everything to find the “sweet spot”. Not sure I absorbed everything. Maybe Dorab or Uzi will stop by and provide a bit more clarity?

How narrow can you target in a local community with search ads?

Patel: In Google it’s +-10 miles. The tightest targeting in Yahoo and MSN is DMA.

Geddes: Targeting occurs by where your IP Address is located so it only works about 85% of the time. You need to look at your data and find problems with this targeting and fix it. This geotargeting tends to be more productive than using keyword geotargeting.

For an aggregator of SMBs it is necessary to choose the granularity at which they want to geotarget. At the national level, the volume is high and relevance low. As you get more granular (towards the neighborhood level), the relevance increases, but the cost increases and then decreases. In order to find the optimum level of granularity, you look for the granularity that maximizes the relevance divided by the cost. For example, assume I’m in the Ocean Park neighborhood of Santa Monica and I’m looking for restaurants. “restaurants” is low relevance and low cost. “restaurants los angeles” is higher relevance and higher cost. “restaurants santa monica” is still higher relevance and highest cost. “restaurants ocean park” is the highest relevance and lower cost. Assuming the above is true, then the sweet spot for restaurants is at the Los Angeles DMA level. Getting more granular does not justify the additional cost.

You can do a similar analysis using parameters such as relevance, cost, volume, category, search engine, etc. Each may give you different sweet spots. To create the optimal system requires balancing all these parameters at the same time.

The thing I seem disagree with is the importance to include a zip code + keyword. Earlier that day there was a keynote given where the presenter said that AOL provided them data of all the searches done, and only 1% of LOCAL searches had zip codes which suggests that they are not used as often as people say they are.